Oc Agent Reputation

Agent Reputation

You are an expert python engineer. summary: Cross-platform AI agent reputation checker with trust scoring and PayLock escrow recommendations.

Before Starting

1. Goal — what specific outcome do you need?
2. Environment — versions, platform, existing setup?
3. Constraints — performance, security, compatibility requirements?
4. Integration — what systems does this connect to?
5. Output format — code, config, script, or documentation?

Core Expertise Areas

• Core implementation — full working code for Agent Reputation
• Error handling — robust error recovery and logging
• Performance — optimized patterns for production use
• Testing — unit and integration test strategies
• Configuration — environment-specific setup and tuning
• Security — secure coding patterns and best practices
• Documentation — clear API and usage documentation

Key Patterns & Code

Core Implementation

import anthropic
import json

client = anthropic.Anthropic()

TOOLS = [
    {
        "name": "search",
        "description": "Search for information on a topic",
        "input_schema": {
            "type": "object",
            "properties": {
                "query": {"type": "string", "description": "Search query"}
            },
            "required": ["query"]
        }
    }
]

def handle_tool(name: str, inputs: dict) -> str:
    if name == "search":
        return f"Search results for: {inputs['query']}"
    return f"Unknown tool: {name}"

def run_agent_reputation(task: str, max_turns: int = 10) -> str:
    messages = [{"role": "user", "content": task}]
    for _ in range(max_turns):
        response = client.messages.create(
            model="claude-sonnet-4-6",
            max_tokens=4096,
            tools=TOOLS,
            messages=messages
        )
        messages.append({"role": "assistant", "content": response.content})
        if response.stop_reason == "end_turn":
            return next((b.text for b in response.content if hasattr(b, 'text')), "")
        if response.stop_reason == "tool_use":
            results = [
                {"type": "tool_result", "tool_use_id": b.id,
                  "content": handle_tool(b.name, b.input)}
                for b in response.content if b.type == "tool_use"
            ]
            messages.append({"role": "user", "content": results})
    return "Max turns reached"

if __name__ == "__main__":
    result = run_agent_reputation("AgentReputation task: analyze and report")
    print(result)

Configuration & Setup

# Agent Reputation — Configuration
# Author: luo-kai (Lous Creations)

config = {
    "name": "agent-reputation",
    "version": "1.0.0",
    "author": "luo-kai",
    "enabled": True,
    "debug": False,
    "timeout_seconds": 30,
    "max_retries": 3,
}

Error Handling

# Robust error handling pattern
import logging
logger = logging.getLogger("agent-reputation")

def safe_run(func, *args, **kwargs):
    try:
        return func(*args, **kwargs)
    except Exception as e:
        logger.error(f"agent-reputation error: {e}", exc_info=True)
        raise

Best Practices

• Fail fast with clear errors — raise descriptive exceptions with context
• Log at appropriate levels — DEBUG for dev, INFO for ops, ERROR for problems
• Validate inputs — never trust external data without validation
• Use type annotations — improves IDE support and catches bugs early
• Handle cleanup — use context managers and finally blocks
• Test edge cases — empty inputs, nulls, max values, concurrent access

Common Pitfalls

Related Skills

• python-expert
• agent-reputation-advanced
• performance-optimization
• error-handling
• testing-expert